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Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
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Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
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Data Communication Based on MQTT in a Polymer Extrusion Process
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A new Apache Spark-based framework for big data streaming forecasting in IoT networks.

Antonio M Fernández-Gómez1, David Gutiérrez-Avilés2, Alicia Troncoso1

  • 1Ctra. de Utrera, km. 1, ES-41013 Seville, Seville Spain Data Science and Big Data Lab, Pablo de Olavide University of Seville.

The Journal of Supercomputing
|February 27, 2023
PubMed
Summary
This summary is machine-generated.

This study presents a novel framework for forecasting big data streams from Internet of Things networks. It integrates network design, streaming architecture, data modeling, and forecasting methods for enhanced analysis.

Keywords:
Big dataIoTStreaming analysisTime series

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Area of Science:

  • Computer Science
  • Data Science
  • Machine Learning

Background:

  • Analyzing time-dependent data streams presents significant challenges across various scientific and industrial domains.
  • The increasing volume and dynamic nature of data from sources like sensors and networks necessitate efficient analytical approaches.
  • Effective big data stream analysis is crucial for optimizing societal production processes and technological advancements.

Purpose of the Study:

  • To introduce a comprehensive framework for forecasting big data streams originating from Internet of Things (IoT) networks.
  • To provide a foundational structure for the design and deployment of third-party solutions in big data stream forecasting.
  • To address the complexities of analyzing time-dependent data in a continuous flow environment.

Main Methods:

  • Development of a novel framework integrating five key modules: IoT network design and deployment, big data streaming architecture, stream data modeling, big data forecasting, and a real-world application scenario.
  • Utilizing a physical IoT network to feed a big data streaming architecture for practical demonstration.
  • Employing linear regression as the algorithm for illustrative forecasting purposes within the framework.

Main Results:

  • The proposed framework successfully integrates all essential modules for time series forecasting in a big data streaming context.
  • Demonstrated the framework's applicability through a real-world IoT network scenario.
  • Established a novel, integrated approach for handling and forecasting continuous data streams.

Conclusions:

  • The presented framework offers a holistic solution for time series forecasting in big data streaming scenarios, particularly for IoT-generated data.
  • This research provides a foundational guide for developing and implementing advanced big data analytics solutions.
  • The integrated nature of the framework distinguishes it as a unique contribution to the field of big data stream analysis.